The present invention relates to digital communications, and more particularly to an improved adaptive equalizer for reducing intersymbol interference in a received signal A method and apparatus are provided for adjusting filter coefficients in a manner that significantly reduces the convergence time of the equalizer.
Digital data, for example digitized video for use in broadcasting high definition television (HDTV) signals, can be transmitted over terrestrial very high frequency (VHF) or ultra high frequency (UHF) analog channels for communication to end users. Analog channels deliver corrupted and transformed versions of their input waveforms. Corruption of the waveform, usually statistical, may be additive and/or multiplicative, because of possible background thermal noise, impulse noise, and fades. Transformations performed by the channel are frequency translation, nonlinear or harmonic distortion, and time dispersion.
In order to communicate digital data via an analog channel, the data is modulated using, for example, a form of pulse amplitude modulation (PAM). Typically, quadrature amplitude modulation (QAM) is used to increase the amount of data that can be transmitted within an available channel bandwidth. QAM is a form of PAM in which a plurality, such as sixteen or thirty-two, bits of information are transmitted together in a pattern referred to as a "constellation".
In pulse amplitude modulation, each signal is a pulse whose amplitude level is determined by a transmitted symbol. In 16-QAM, symbol amplitudes of -3, -1, 1 and 3 in each quadrature channel are typically used. In bandwidth efficient digital communication systems, the effect of each symbol transmitted over a time-dispersive channel extends beyond the time interval used to represent that symbol. The distortion caused by the resulting overlap of received symbols is called intersymbol interference (ISI). This distortion has been one of the major obstacles to reliable high speed data transmission over low background noise channels of limited bandwidth. A device known as an "equalizer" is used to deal with the ISI problem.
In order to reduce the intersymbol interference introduced by a communication channel, rather precise equalization is required Furthermore, the channel characteristics are typically not known beforehand. Thus, it is common to design and use a compromise (or a statistical) equalizer that compensates for the average of the range of expected channel amplitude and delay characteristics. A least mean square (LMS) error adaptive filtering scheme has been in common use as an adaptive equalization algorithm for over 20 years. This algorithm is described in B. Widrow and M. E. Hoff, Jr., "Adaptive Switching Circuits" in IRE Wescon Conv. Rec., Part 4, pp. 96-104, August 1960. The use of the LMS algorithm in an adaptive equalizer to reduce intersymbol interference is discussed in S. U. H. Qureshi, "Adaptive Equalization", Proc. IEEE, Vol. 73, No. 9, pp. 1349-1387, September 1987.
In an LMS equalizer, the equalizer filter coefficients are chosen to minimize the mean square error, i.e., the sum of squares of all the ISI terms plus the noise power at the output of the equalizer. Therefore, the LMS equalizer maximizes the signal-to-distortion ratio at its output within the constraints of the equalizer time span and the delay through the equalizer. Before regular data transmission begins, automatic synthesis of the LMS equalizer for unknown channels may be carried out during a training period. This generally involves the iterative solution of a set of simultaneous equations. During the training period, a known signal is transmitted and a synchronized version of the signal is generated in the receiver to acquire information about the channel characteristics. The training signal may consist of periodic isolated pulses or a continuous sequence with a broad, uniform spectrum such as a widely known maximum length shift register or pseudo-noise sequence.
An important aspect of equalizer performance is its convergence, which is generally measured by the amount of time in symbol periods required for the error variance in the equalizer to settle at a minimum level, which is ideally zero. In order to obtain the most efficient operation for a data receiver, the equalizer convergence time must be minimized.
After any initial training period, the coefficients of an adaptive equalizer may be continually adjusted in a decision directed manner. In this mode, the error signal is derived from the final receiver estimate (not necessarily correct) of the transmitted sequence. In normal operation, the receiver decisions are correct with high probability, so that the error estimates are correct often enough to allow the adaptive equalizer to maintain precise equalization. Moreover, a decision directed adaptive equalizer can track slow variations in the channel characteristics or linear perturbations in the receiver front end, such as slow jitter in the sampler phase.
The larger the step size, the faster the equalizer tracking capability. However, a compromise must be made between fast tracking and the excess mean square error (MSE) of the equalizer. The excess MSE is that part of the error power in excess of the minimum attainable MSE (with tap gains frozen at their optimum settings). This excess MSE, caused by tap gains wandering around the optimum settings, is directly proportional to the number of equalizer coefficients, the step size, and the channel noise power.
Many transmission systems employ modulation schemes that are constructed with complex signal sets. In other words, the signals are viewed as vectors in the complex plane, with the real axis called the inphase (I) channel and the imaginary axis called the quadrature (Q) channel. Consequently, when these signals are subjected to channel distortion and receiver impairments, cross talk between the I and Q channels occurs, requiring a complex adaptive equalizer. In this case, the equalizer's coefficients will be complex valued. If, as noted above, the channel distortion is unknown by the receiver, then the coefficients must be adjusted after the system has been in operation to cancel the channel distortion. The term "adaptive" in a complex adaptive equalizer signifies the ongoing adjustment of the coefficients.
Prior art adaptive equalizers, including complex adaptive equalizers, have suffered from a relatively long convergence time of the LMS algorithm. Alternate algorithms, such as the recursive least squares (RLS) algorithm have been developed in order to overcome this disadvantage, and the RLS algorithm does indeed converge faster than LMS. However, RLS is more complex to implement than LMS and there are also numerical stability problems associated with the RLS algorithm. Therefore, prior art designs have tolerated the longer convergence time of the LMS implementation in order to avoid the disadvantages of the RLS scheme.
Even though the LMS algorithm is less complex to implement than other algorithms such as RLS, substantial hardware is still required to implement the algorithm in systems where floating point signal processors are not fast enough. It would therefore be advantageous to provide an implementation of the LMS algorithm that minimizes hardware without sacrificing system performance. It would be further advantageous to provide an LMS adaptive equalizer having improved convergence performance (i.e., faster convergence time) without undue added complexity. It would be still further advantageous to provide such an adaptive equalizer that is easily implemented in an integrated circuit, such as in a very large scale integration (VLSI) device.
The present invention provides an adaptive equalizer having the above-noted advantages.